1. Field of the Invention
The present invention relates to technology for detecting a user's hand region in an image, and more specifically, to a user hand detecting device for detecting a user's hand region in smart glasses.
2. Discussion of Related Art
Recently, various companies including Google Inc (Google Glass) have released various optical see-through smart glasses. Unlike video see-through head mounted displays (HMDs) in the related art, smart glasses can combine information observed by a user with an object of a real world and show the result.
As a method of interacting with content output to such smart glasses, methods in which a touch sensor, speech recognition, and gesture recognition using camera vision are used are proposed. For example, Google Glass (Google Inc) uses a touch pad of an eyeglass frame and speech recognition, and Space Glasses (Meta Company) uses a hand gesture recognition method using a time of flight (ToF) camera. Among them, a method of providing the most natural user interfaces/user experience (UI/UX) to a user is a method of performing an interaction using a hand with respect to content to be output.
Here, hand gesture recognition includes an accurate hand position detecting operation, a tracking and segmentation operation, and a recognition operation. In order to recognize a hand gesture accurately, the previous hand position detecting operation and tracking and segmentation operation are very important.
Meanwhile, a camera-based hand gesture recognition method includes a method in which color information obtained by a skin color model of a color camera is used, a method in which 3D depth information of an object obtained by a stereo camera, a ToF camera or the like is used, and a hybrid method in which two methods are combined.
Also, as a method in which skin color information is used in a color image, there are various methods such as a Gaussian mixture model. However, there is a problem in that a large amount of data is necessary to generate skin color information, and detection is difficult based on unlearned data.
Also, a method of segmenting a hand position using a 3D depth image has a problem in that it is difficult to segment a hand region precisely in depth information according to a change in an ambient environment (such as a texture).